{
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  "metadata": {
    "colab": {
      "name": "Pressure_BuildUp_Test.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyM+rbXob7fJMA9tQpkeK7bA",
      "include_colab_link": true
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    "kernelspec": {
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Divyanshu-ISM/Oil-and-Gas-data-analysis/blob/master/Pressure_BuildUp_Test.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5kWCwWEKMj9m",
        "colab_type": "text"
      },
      "source": [
        "#**Well Test Analysis with Python** - Part 2\n",
        "\n",
        "##Pressure Build-up tests. \n",
        "\n",
        "Author : Divyanshu Vyas | PE/RE | ML-Deep Learning\n",
        "Contact : dvyas13ad@gmail.com"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7MP_L0tUM3oB",
        "colab_type": "text"
      },
      "source": [
        "Problem statement:\n",
        "![image.png]()"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Aq5vJK_9mwL2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "%matplotlib inline"
      ],
      "execution_count": 74,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S57f1yCOnANE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df = pd.read_excel('buildup tutorial soln2.xlsx',sheet_name='Sheet2')"
      ],
      "execution_count": 75,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tFZcVYCUoGSl",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "outputId": "0bea6e01-79b3-40c7-f965-f128ae44f26e"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>t (hrs)</th>\n",
              "      <th>Pws</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.0000</td>\n",
              "      <td>4192.887177</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.0010</td>\n",
              "      <td>4196.716637</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.0021</td>\n",
              "      <td>4201.194377</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.0034</td>\n",
              "      <td>4207.221945</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.0048</td>\n",
              "      <td>4212.246464</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   t (hrs)          Pws\n",
              "0   0.0000  4192.887177\n",
              "1   0.0010  4196.716637\n",
              "2   0.0021  4201.194377\n",
              "3   0.0034  4207.221945\n",
              "4   0.0048  4212.246464"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "37bEMpgyoLTW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_analysis = df.loc[1:,:]"
      ],
      "execution_count": 77,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pFgPEKBtobTk",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "outputId": "f85792ac-03d2-4803-8a17-49c1cea893fa"
      },
      "source": [
        "df_analysis.head()\n",
        "#note that the t(hrs) column is for the shut in time (dt)"
      ],
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>t (hrs)</th>\n",
              "      <th>Pws</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.0010</td>\n",
              "      <td>4196.716637</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.0021</td>\n",
              "      <td>4201.194377</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.0034</td>\n",
              "      <td>4207.221945</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.0048</td>\n",
              "      <td>4212.246464</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.0064</td>\n",
              "      <td>4219.681700</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   t (hrs)          Pws\n",
              "1   0.0010  4196.716637\n",
              "2   0.0021  4201.194377\n",
              "3   0.0034  4207.221945\n",
              "4   0.0048  4212.246464\n",
              "5   0.0064  4219.681700"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s0t6gasJodSr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "tp = 168 #hrs"
      ],
      "execution_count": 79,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NU6h2Y8NojC6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 138
        },
        "outputId": "73af0f80-d708-454e-e3b4-55e62b3e27c7"
      },
      "source": [
        "df_analysis['th = (tp+dt)/dt'] = (df_analysis['t (hrs)'] + tp)/df_analysis['t (hrs)']"
      ],
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KYanbXhGpCPC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# df_analysis.reset_index(inplace=True , drop = True)\n",
        "# df_analysis.head()"
      ],
      "execution_count": 81,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xfp_WtQbpEyH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 138
        },
        "outputId": "4ed77374-25bd-4add-aaa4-5134877d12f8"
      },
      "source": [
        "df_analysis['te'] = tp/df_analysis['th = (tp+dt)/dt']"
      ],
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ivSIGu1UpXw7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "outputId": "f1ccd4b0-61b3-42c8-f066-2a9aae28797f"
      },
      "source": [
        "df_analysis.head()"
      ],
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>t (hrs)</th>\n",
              "      <th>Pws</th>\n",
              "      <th>th = (tp+dt)/dt</th>\n",
              "      <th>te</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.0010</td>\n",
              "      <td>4196.716637</td>\n",
              "      <td>168001.000000</td>\n",
              "      <td>0.0010</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.0021</td>\n",
              "      <td>4201.194377</td>\n",
              "      <td>80001.000000</td>\n",
              "      <td>0.0021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.0034</td>\n",
              "      <td>4207.221945</td>\n",
              "      <td>49412.764706</td>\n",
              "      <td>0.0034</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.0048</td>\n",
              "      <td>4212.246464</td>\n",
              "      <td>35001.000000</td>\n",
              "      <td>0.0048</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.0064</td>\n",
              "      <td>4219.681700</td>\n",
              "      <td>26251.000000</td>\n",
              "      <td>0.0064</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   t (hrs)          Pws  th = (tp+dt)/dt      te\n",
              "1   0.0010  4196.716637    168001.000000  0.0010\n",
              "2   0.0021  4201.194377     80001.000000  0.0021\n",
              "3   0.0034  4207.221945     49412.764706  0.0034\n",
              "4   0.0048  4212.246464     35001.000000  0.0048\n",
              "5   0.0064  4219.681700     26251.000000  0.0064"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VRoci95vrVZr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 118
        },
        "outputId": "4abc9e5f-b98c-4216-ab8c-ede5d9d032fe"
      },
      "source": [
        "df['Pws'][0:5]"
      ],
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    4192.887177\n",
              "1    4196.716637\n",
              "2    4201.194377\n",
              "3    4207.221945\n",
              "4    4212.246464\n",
              "Name: Pws, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-BqyoHSorbNW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 118
        },
        "outputId": "3f97be5e-8847-4f99-d294-a64fe515cede"
      },
      "source": [
        "df_analysis['Pws'][:5]"
      ],
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    4196.716637\n",
              "2    4201.194377\n",
              "3    4207.221945\n",
              "4    4212.246464\n",
              "5    4219.681700\n",
              "Name: Pws, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1Wj1YFjnr9NP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 85,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GU8hn2w-paB7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 138
        },
        "outputId": "814741aa-6fd7-481c-a397-8c33e446bef9"
      },
      "source": [
        "df_analysis['dp'] = df_analysis['Pws'][0:] - df['Pws'][:-1]"
      ],
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b0Hy7x3asGsl",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 118
        },
        "outputId": "e8b7e128-4835-42a8-ae6b-573377196629"
      },
      "source": [
        "df_analysis['dp'].head()"
      ],
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    0.0\n",
              "2    0.0\n",
              "3    0.0\n",
              "4    0.0\n",
              "5    0.0\n",
              "Name: dp, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 87
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H57a9sOEqLZt",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 138
        },
        "outputId": "a6a8a943-f558-4b09-da13-614f97db0cc8"
      },
      "source": [
        "df_analysis['dp/dt_e'] = df_analysis['dp']/(df_analysis['te'][1:] - df_analysis['te'][:-1])"
      ],
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SPhkuga-tzWh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 121
        },
        "outputId": "83af105a-0098-431a-c9b2-6b65dd6c5ab9"
      },
      "source": [
        "df_analysis.drop('dp',axis=1,inplace=True)"
      ],
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/pandas/core/frame.py:3997: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  errors=errors,\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m7BHU1ket7pY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 138
        },
        "outputId": "16067428-18c0-4224-87d5-3422365f5fef"
      },
      "source": [
        "df_analysis['dP'] = df['Pws'][1:] - df['Pws'][0:-1]"
      ],
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Yji4iSpDuIYA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "outputId": "61fe5bbd-ced6-46b8-dcad-24825e995d35"
      },
      "source": [
        "df_analysis.head()"
      ],
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>t (hrs)</th>\n",
              "      <th>Pws</th>\n",
              "      <th>th = (tp+dt)/dt</th>\n",
              "      <th>te</th>\n",
              "      <th>dp/dt_e</th>\n",
              "      <th>dP</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.0010</td>\n",
              "      <td>4196.716637</td>\n",
              "      <td>168001.000000</td>\n",
              "      <td>0.0010</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.0021</td>\n",
              "      <td>4201.194377</td>\n",
              "      <td>80001.000000</td>\n",
              "      <td>0.0021</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.0034</td>\n",
              "      <td>4207.221945</td>\n",
              "      <td>49412.764706</td>\n",
              "      <td>0.0034</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.0048</td>\n",
              "      <td>4212.246464</td>\n",
              "      <td>35001.000000</td>\n",
              "      <td>0.0048</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.0064</td>\n",
              "      <td>4219.681700</td>\n",
              "      <td>26251.000000</td>\n",
              "      <td>0.0064</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   t (hrs)          Pws  th = (tp+dt)/dt      te  dp/dt_e   dP\n",
              "1   0.0010  4196.716637    168001.000000  0.0010      NaN  0.0\n",
              "2   0.0021  4201.194377     80001.000000  0.0021      NaN  0.0\n",
              "3   0.0034  4207.221945     49412.764706  0.0034      NaN  0.0\n",
              "4   0.0048  4212.246464     35001.000000  0.0048      NaN  0.0\n",
              "5   0.0064  4219.681700     26251.000000  0.0064      NaN  0.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 91
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "51BEAvrOtbYV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# df_analysis['dp'][0]/(df_analysis['te'][1] - df_analysis['te'][0])"
      ],
      "execution_count": 93,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KQyYdDXUqyIg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_analysis.head()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K8_r8iSGq0jy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df.head()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "71966NuZuuLB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# len(df) #86\n",
        "Pws = np.array(df['Pws'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wVi9G96guxr9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "dp = np.array(Pws[1:] - Pws[0:-1])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sXsRkimevsQh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "te = np.array(df_analysis['te'])\n",
        "dt_e = np.array(te[1:]-te[0:-1])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UW7YRa5VwHxs",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "dpdt_e = dp[1:]/dt_e\n",
        "# dpdt_e = dPdt_e + np.zeros(85)\n",
        "dpdt_e = list(dpdt_e)\n",
        "dpdt_e.append(0)\n",
        "dpdt_e = np.array(dpdt_e)"
      ],
      "execution_count": 94,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bGx_TxbEw506",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "izwx49uUwJ9l",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 138
        },
        "outputId": "db259afc-5c5f-4a0c-b2b3-ad2a0fa468cc"
      },
      "source": [
        "df_analysis['dp/dte']  = dpdt_e"
      ],
      "execution_count": 95,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PjjFKy95whqB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "outputId": "515763b6-460b-4132-d4e8-2694b7347662"
      },
      "source": [
        "df_analysis.head()"
      ],
      "execution_count": 96,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>t (hrs)</th>\n",
              "      <th>Pws</th>\n",
              "      <th>th = (tp+dt)/dt</th>\n",
              "      <th>te</th>\n",
              "      <th>dp/dt_e</th>\n",
              "      <th>dP</th>\n",
              "      <th>dp/dte</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.0010</td>\n",
              "      <td>4196.716637</td>\n",
              "      <td>168001.000000</td>\n",
              "      <td>0.0010</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4070.748572</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.0021</td>\n",
              "      <td>4201.194377</td>\n",
              "      <td>80001.000000</td>\n",
              "      <td>0.0021</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4636.742365</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.0034</td>\n",
              "      <td>4207.221945</td>\n",
              "      <td>49412.764706</td>\n",
              "      <td>0.0034</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3589.117253</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.0048</td>\n",
              "      <td>4212.246464</td>\n",
              "      <td>35001.000000</td>\n",
              "      <td>0.0048</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4647.332073</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.0064</td>\n",
              "      <td>4219.681700</td>\n",
              "      <td>26251.000000</td>\n",
              "      <td>0.0064</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4305.343570</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   t (hrs)          Pws  th = (tp+dt)/dt      te  dp/dt_e   dP       dp/dte\n",
              "1   0.0010  4196.716637    168001.000000  0.0010      NaN  0.0  4070.748572\n",
              "2   0.0021  4201.194377     80001.000000  0.0021      NaN  0.0  4636.742365\n",
              "3   0.0034  4207.221945     49412.764706  0.0034      NaN  0.0  3589.117253\n",
              "4   0.0048  4212.246464     35001.000000  0.0048      NaN  0.0  4647.332073\n",
              "5   0.0064  4219.681700     26251.000000  0.0064      NaN  0.0  4305.343570"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 96
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "liAgHPBYy4s-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 138
        },
        "outputId": "3fd68156-5612-4dba-feff-c4c0e38fce4a"
      },
      "source": [
        "df_analysis['te*dp/dt_e'] = df_analysis['dp/dte']*df_analysis['te']"
      ],
      "execution_count": 99,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OGua3VP3zGTM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "outputId": "e30ea06e-8fba-4be1-9c5c-8bc774b98cda"
      },
      "source": [
        "df_analysis.head()"
      ],
      "execution_count": 100,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>t (hrs)</th>\n",
              "      <th>Pws</th>\n",
              "      <th>th = (tp+dt)/dt</th>\n",
              "      <th>te</th>\n",
              "      <th>dp/dt_e</th>\n",
              "      <th>dP</th>\n",
              "      <th>dp/dte</th>\n",
              "      <th>te*dp/dt_e</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.0010</td>\n",
              "      <td>4196.716637</td>\n",
              "      <td>168001.000000</td>\n",
              "      <td>0.0010</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4070.748572</td>\n",
              "      <td>4.070724</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.0021</td>\n",
              "      <td>4201.194377</td>\n",
              "      <td>80001.000000</td>\n",
              "      <td>0.0021</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4636.742365</td>\n",
              "      <td>9.737037</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.0034</td>\n",
              "      <td>4207.221945</td>\n",
              "      <td>49412.764706</td>\n",
              "      <td>0.0034</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3589.117253</td>\n",
              "      <td>12.202752</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.0048</td>\n",
              "      <td>4212.246464</td>\n",
              "      <td>35001.000000</td>\n",
              "      <td>0.0048</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4647.332073</td>\n",
              "      <td>22.306557</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.0064</td>\n",
              "      <td>4219.681700</td>\n",
              "      <td>26251.000000</td>\n",
              "      <td>0.0064</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4305.343570</td>\n",
              "      <td>27.553149</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   t (hrs)          Pws  th = (tp+dt)/dt  ...   dP       dp/dte  te*dp/dt_e\n",
              "1   0.0010  4196.716637    168001.000000  ...  0.0  4070.748572    4.070724\n",
              "2   0.0021  4201.194377     80001.000000  ...  0.0  4636.742365    9.737037\n",
              "3   0.0034  4207.221945     49412.764706  ...  0.0  3589.117253   12.202752\n",
              "4   0.0048  4212.246464     35001.000000  ...  0.0  4647.332073   22.306557\n",
              "5   0.0064  4219.681700     26251.000000  ...  0.0  4305.343570   27.553149\n",
              "\n",
              "[5 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 100
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JcOy3P48zWFm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 138
        },
        "outputId": "f74fccae-3a11-4371-e72e-deb8de25c586"
      },
      "source": [
        "df_analysis['DeltaP'] = df_analysis['Pws'] -4192.89"
      ],
      "execution_count": 101,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JB8l7yXw0E-I",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "outputId": "727ea418-c932-452f-abaf-94d451e908fe"
      },
      "source": [
        "df_analysis.head()"
      ],
      "execution_count": 102,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>t (hrs)</th>\n",
              "      <th>Pws</th>\n",
              "      <th>th = (tp+dt)/dt</th>\n",
              "      <th>te</th>\n",
              "      <th>dp/dt_e</th>\n",
              "      <th>dP</th>\n",
              "      <th>dp/dte</th>\n",
              "      <th>te*dp/dt_e</th>\n",
              "      <th>DeltaP</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.0010</td>\n",
              "      <td>4196.716637</td>\n",
              "      <td>168001.000000</td>\n",
              "      <td>0.0010</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4070.748572</td>\n",
              "      <td>4.070724</td>\n",
              "      <td>3.826637</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.0021</td>\n",
              "      <td>4201.194377</td>\n",
              "      <td>80001.000000</td>\n",
              "      <td>0.0021</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4636.742365</td>\n",
              "      <td>9.737037</td>\n",
              "      <td>8.304377</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.0034</td>\n",
              "      <td>4207.221945</td>\n",
              "      <td>49412.764706</td>\n",
              "      <td>0.0034</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3589.117253</td>\n",
              "      <td>12.202752</td>\n",
              "      <td>14.331945</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.0048</td>\n",
              "      <td>4212.246464</td>\n",
              "      <td>35001.000000</td>\n",
              "      <td>0.0048</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4647.332073</td>\n",
              "      <td>22.306557</td>\n",
              "      <td>19.356464</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.0064</td>\n",
              "      <td>4219.681700</td>\n",
              "      <td>26251.000000</td>\n",
              "      <td>0.0064</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4305.343570</td>\n",
              "      <td>27.553149</td>\n",
              "      <td>26.791700</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   t (hrs)          Pws  th = (tp+dt)/dt  ...       dp/dte  te*dp/dt_e     DeltaP\n",
              "1   0.0010  4196.716637    168001.000000  ...  4070.748572    4.070724   3.826637\n",
              "2   0.0021  4201.194377     80001.000000  ...  4636.742365    9.737037   8.304377\n",
              "3   0.0034  4207.221945     49412.764706  ...  3589.117253   12.202752  14.331945\n",
              "4   0.0048  4212.246464     35001.000000  ...  4647.332073   22.306557  19.356464\n",
              "5   0.0064  4219.681700     26251.000000  ...  4305.343570   27.553149  26.791700\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 102
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mc8pbG8f0HKR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 415
        },
        "outputId": "9083f718-d919-41b3-e102-4e6b80987c64"
      },
      "source": [
        "plt.style.use('default')\n",
        "plt.figure(figsize=(8,4))\n",
        "plt.loglog(df_analysis['te'], df_analysis['te*dp/dt_e'],linewidth=1, label ='Derivative Plot',marker='o')\n",
        "plt.xlim(0.0001,80)\n",
        "\n",
        "plt.xlabel('Time Axis')\n",
        "plt.ylabel('Pressure(related) axis')\n",
        "\n",
        "plt.loglog(df_analysis['te'], df_analysis['DeltaP'],linewidth=1, label = 'DeltaP v/s t_eff',marker='*')\n",
        "\n",
        "plt.title('Build-Up Test Diagnostic Plot')\n",
        "plt.legend(loc='best')\n",
        "plt.grid()"
      ],
      "execution_count": 116,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 800x400 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CUiq6ZLHNBBB",
        "colab_type": "text"
      },
      "source": [
        "#Conclusive Remarks and steps forward:\n",
        "1. We've found the IARF (horizontal line) and that's the main job of our analysis. \n",
        "2. We can spot the USL and that tels us the WelBore Storage Region. \n",
        "3. We can take Data from IARF and find the permeability."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QDYKAWiN0YhP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}